## Error in library(future.apply) : 
##   there is no package called 'future.apply'

Project info

Samples: dcDNA Seq
pychopper: no
mapping version: v6
LoRTIA: yes (stranded only output)

Normalization: NO (raw read counts)

TSS dynamics

Those reads were counted only, where the 5-prime adapters were “correct” (according to LoRTIA)

Statistics of counts

Difference from mean

Varcoeff

Clustering of samples

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The sample 48h_3 stands out!

Filtering of samples

Clustering of genes into de-novo clusters using hierarchial clustering

Cluster memberships

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Optimal number of clusters

Cut the tree at an optimal number of clusters

Clustering statistics: Cluster number: 5

## [1] "ORF74" "ORF41"
## [1] 2

Clustering statistics: Cluster number: 6

## [1] "ORF63" "ORF74" "CTO"   "ORF41"
## [1] 4

Clustering statistics: Cluster number: 7

## [1] "ORF63" "ORF74"
## [1] 2

Selected number of clusters: 7

## [1] "ORF63" "ORF74"
## [1] 2

Remove genes from clusters ?

## The following genes were removed from their corresponding clusters 
##            as their silhouette widths were lower than 0.1 and their distance from the group centroid was higher than 0.5: 
## ORF63
## ORF74

Assigning genes to clusters

Summarize and melt

Heatmaps

Normalized Expression

Expression delta ~ 6 hpi

Z-score of expression delta ~ 6 hpi

ORF kinetics, according to literature-based kinetic classes

Barplot of mean and SD

Figure 4 REVIEW - Mean and SD with linear connection

Mean and SD with loess function

Mean and SD with linear connection - Figure 4

Each replicate with loess

ORF kinetics, according to de-novo kinetic classes

Mean and SD with linear connection

Figure 5 REVIEW - Mean and SD with linear connection

Compare literature and de novo kinetic classes

NA shows the number of genes where the clustering did not yield an unambiguous result.

Combined Kinetic classes

De Novo Clusters

Literature clusters